Upload folder using huggingface_hub
Browse files- README.md +58 -0
- cubicasa5k/checkpoint.pth +3 -0
- cubicasa5k/config.json +26 -0
- manifest.json +45 -0
- raster2graph/checkpoint.pth +3 -0
- raster2graph/config.json +26 -0
- s3d-bw/checkpoint.pth +3 -0
- s3d-bw/config.json +26 -0
- s3d-density/checkpoint.pth +3 -0
- s3d-density/config.json +26 -0
README.md
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---
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license: mit
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---
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---
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library_name: pytorch
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license: mit
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tags:
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- computer-vision
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- floorplan-reconstruction
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- pytorch
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- raster2seq
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---
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# Raster2Seq Checkpoints
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This repository hosts Raster2Seq PyTorch checkpoints for polygon sequence generation from rasterized floorplans. Each checkpoint is stored in its own subfolder so users can download all checkpoints or only the subfolder they need.
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## Available Checkpoints
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| Checkpoint key | Dataset | RoomF1 | Subfolder |
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| --- | --- | ---: | --- |
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| `s3d-bw` | Structured3D-B | 99.6 | `s3d-bw/` |
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| `cubicasa5k` | CubiCasa5K | 88.7 | `cubicasa5k/` |
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| `raster2graph` | Raster2Graph | 97.0 | `raster2graph/` |
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| `s3d-density` | Structured3D-DensityMap | 99.1 | `s3d-density/` |
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## Download All Checkpoints
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```python
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from huggingface_hub import snapshot_download
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local_repo = snapshot_download(repo_id="haopt/Raster2Seq")
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```
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## Download One Checkpoint Subfolder
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```python
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from huggingface_hub import snapshot_download
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local_repo = snapshot_download(
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repo_id="haopt/Raster2Seq",
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allow_patterns="cubicasa5k/*",
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)
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```
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## Raster2Seq Helper
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With the Raster2Seq codebase, users can load by alias:
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```bash
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python eval.py --checkpoint hf:cubicasa5k ...
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```
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or download directly:
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```python
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from raster2seq_hub import download_checkpoint
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ckpt_path = download_checkpoint("cubicasa5k")
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```
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The helper downloads the matching subfolder and returns the cached `checkpoint.pth` path.
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Hugging Face repository IDs use the form `namespace/repo`. Dataset-specific choices are represented by subfolders, not by repo IDs such as `Raster2Seq/cubicasa5k`.
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cubicasa5k/checkpoint.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:f7c0ef9379aa8cf11349da8b2179214e0d495af758383984a2361a1309aab3b7
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size 1452141664
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cubicasa5k/config.json
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{
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"checkpoint_key": "cubicasa5k",
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"name": "CubiCasa5K",
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"checkpoint_filename": "checkpoint.pth",
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"source_local_filename": "cc5k_sem_res256_ep0499.pth",
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"metrics": {
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"room_f1": 88.7
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},
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"inference_args": {
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"dataset_name": "cubicasa",
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"dataset_root": "data/coco_cubicasa5k_nowalls_v4-1_refined/",
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"eval_set": "test",
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"output_dir": "eval_outputs/cc5k_sem_results/",
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"semantic_classes": 12,
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"input_channels": 3,
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"poly2seq": true,
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"seq_len": 512,
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"num_bins": 32,
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"disable_poly_refine": true,
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"dec_attn_concat_src": true,
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"ema4eval": true,
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"use_anchor": true,
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"per_token_sem_loss": true,
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"save_pred": true
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}
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}
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manifest.json
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{
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"repo_id": "haopt/Raster2Seq",
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"checkpoint_filename": "checkpoint.pth",
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"checkpoints": {
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"s3d-bw": {
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"name": "Structured3D-B",
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"subfolder": "s3d-bw",
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"local_filename": "s3dbw_sem_res256_ep0449.pth",
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"room_f1": 99.6,
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"config": "s3d-bw/config.json"
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},
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"cubicasa5k": {
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"name": "CubiCasa5K",
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"subfolder": "cubicasa5k",
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"local_filename": "cc5k_sem_res256_ep0499.pth",
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"room_f1": 88.7,
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"config": "cubicasa5k/config.json"
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},
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"raster2graph": {
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"name": "Raster2Graph",
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"subfolder": "raster2graph",
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"local_filename": "r2g_sem_res256_ep0549.pth",
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"room_f1": 97.0,
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"config": "raster2graph/config.json"
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},
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"s3d-density": {
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"name": "Structured3D-DensityMap",
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"subfolder": "s3d-density",
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"local_filename": "s3dd_sem_res256_ep0699.pth",
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"room_f1": 99.1,
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"config": "s3d-density/config.json"
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}
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},
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"aliases": {
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"s3d": "s3d-bw",
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"structured3d": "s3d-bw",
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"structured3d-b": "s3d-bw",
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"s3dbw": "s3d-bw",
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"cc5k": "cubicasa5k",
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"cubicasa": "cubicasa5k",
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"r2g": "raster2graph",
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"s3dd": "s3d-density",
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"structured3d-density": "s3d-density"
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}
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}
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raster2graph/checkpoint.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:ac0ebb526a433ca2a4391362ee602869161dc052a4ff45b09d25dc7b85105610
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size 1027373728
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raster2graph/config.json
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{
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"checkpoint_key": "raster2graph",
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"name": "Raster2Graph",
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"checkpoint_filename": "checkpoint.pth",
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"source_local_filename": "r2g_sem_res256_ep0549.pth",
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"metrics": {
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"room_f1": 97.0
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},
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"inference_args": {
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"dataset_name": "r2g",
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"dataset_root": "data/R2G_hr_dataset_processed_v1",
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"eval_set": "test",
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"output_dir": "eval_outputs/r2g_sem_results",
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"semantic_classes": 13,
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"input_channels": 3,
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"poly2seq": true,
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"seq_len": 512,
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"num_bins": 32,
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"disable_poly_refine": true,
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"dec_attn_concat_src": true,
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"ema4eval": true,
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"use_anchor": true,
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"per_token_sem_loss": true,
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"save_pred": true
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}
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}
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s3d-bw/checkpoint.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:f5b82e15e033f1394dc6bfb7466396da30f04a76cbb1c1ec44024c1e60702fd0
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size 983456736
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s3d-bw/config.json
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{
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"checkpoint_key": "s3d-bw",
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"name": "Structured3D-B",
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"checkpoint_filename": "checkpoint.pth",
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"source_local_filename": "s3dbw_sem_res256_ep0449.pth",
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"metrics": {
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| 7 |
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"room_f1": 99.6
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},
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"inference_args": {
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"dataset_name": "stru3d",
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"dataset_root": "data/coco_s3d_bw",
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"eval_set": "test",
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"output_dir": "eval_outputs/s3dbw_sem_results",
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"semantic_classes": 19,
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"input_channels": 3,
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"poly2seq": true,
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"seq_len": 512,
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| 18 |
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"num_bins": 32,
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"disable_poly_refine": true,
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| 20 |
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"dec_attn_concat_src": true,
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| 21 |
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"ema4eval": true,
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| 22 |
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"use_anchor": true,
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| 23 |
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"per_token_sem_loss": true,
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"save_pred": true
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}
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}
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s3d-density/checkpoint.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:35c7f69b8142ec26734a9f69645b5b32235030ed5e577951bc19f16c6c65b69b
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size 983406560
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s3d-density/config.json
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{
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"checkpoint_key": "s3d-density",
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| 3 |
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"name": "Structured3D-DensityMap",
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| 4 |
+
"checkpoint_filename": "checkpoint.pth",
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| 5 |
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"source_local_filename": "s3dd_sem_res256_ep0699.pth",
|
| 6 |
+
"metrics": {
|
| 7 |
+
"room_f1": 99.1
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| 8 |
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},
|
| 9 |
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"inference_args": {
|
| 10 |
+
"dataset_name": "stru3d",
|
| 11 |
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"dataset_root": "data/stru3d",
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| 12 |
+
"eval_set": "test",
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| 13 |
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"output_dir": "eval_outputs/s3dd_sem_results",
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| 14 |
+
"semantic_classes": 19,
|
| 15 |
+
"input_channels": 1,
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| 16 |
+
"poly2seq": true,
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| 17 |
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"seq_len": 512,
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| 18 |
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"num_bins": 32,
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| 19 |
+
"disable_poly_refine": true,
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| 20 |
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"dec_attn_concat_src": true,
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| 21 |
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"ema4eval": true,
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| 22 |
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"use_anchor": true,
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| 23 |
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"per_token_sem_loss": true,
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| 24 |
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"save_pred": true
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| 25 |
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}
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| 26 |
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}
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